The energy landscape for the Low-Voltage (LV) networks are beginning to change; changes resulted from the increase penetration of renewables and/or the predicted increase of electric vehicles charging at home. The previously passive 'fit-and-forget' approach to LV network management will be inefficient to ensure its effective operations. A more adaptive approach is required that includes the prediction of risk and capacity of the circuits. Many of the proposed methods require full observability of the networks, motivating the installations of smart meters and advance metering infrastructure in many countries. However, the expectation of 'perfect data' is unrealistic in operational reality. Smart meter (SM) roll-out can have its issues, which may resulted in low-likelihood of full SM coverage for all LV networks. This, together with privacy requirements that limit the availability of high granularity demand power data have resulted in the low uptake of many of the presented methods. To address this issue, Deep Learning Neural Network is proposed to predict the voltage distribution with partial SM coverage. The results show that SM measurements from key locations are sufficient for effective prediction of voltage distribution.
SP Energy Networks (SPEN) was involved in the research program Smart Building Potential within Heavily Utilised Networks to assess the role that Demand-Side Response (DSR) could play in providing the distribution network operators (DNO) with an economic and readily deployable means of enhancing the capability of their networks. DSR trials were carried out across 10 city centre buildings in Glasgow. The average level of demand reduction of controllable loads across the whole year was found to be around 20%, which equated to a 7% reduction overall. Comms issues were found in some buildings, which resulted in gaps in data. Network models were developed that allowed the impact of DSRtaking the levels identified through live trialsto be modelled and a business case developed. The business case suggested that DSR is a cost-effective network support tool if low levels of load reduction are required. As the level of load reduction increases, traditional reinforcement is more cost effective.
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